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airflow-demo-automation

Automating Data Science pipeline using airflow

association-rules-on-soccer-teams

big-mart-sales-prediction

Basic EDA & data manipulation for Big Mart Sales

boosting-algo-gbm-xgboost-python

Building model using GBM and XGBoost in Python

decision-boundary-visualization

Visualizing how predictive algorithms works can be challenging at times. With more focus on mathematics, we tend to miss out of intuition behind these models. Generating decision boundaries for different hyper parameter values can not only improve our understanding but also help to overcome over fitting..

ensemble-practice

Building different model and creating ensemble of models

gbm-xgboost-pyspark

Implementation of boosting algorithms in PySpark with local Spark cluster

gbm-xgboost-sparklyr

Implementation of boosting algorithms in SparklyR with local Spark cluster

image-processing-mnist-data

Building neural network and performing image processing with the help of MNIST dataset

k-means-clustering

Learning k-means clustering and it performance improvement

kaggle-dogs_vs_cats-cnn-keras

Practiced CNN implementation using Kaggle competition of Dogs vs Cats

kaggle-donorschoose-nlp-lightgbm

Using LightGBM and NLP to predict the application approval for Kaggle Competitions DonorsChoose.org

keras-resources

Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library

multinomial-logistic-using-glass-dataset

Building multi nomial logistic regression model using glass dataset

nested-cross-validation

Comparison among models can be tricky and tedious task due to multiple hyper parameter settings and random split of data. Nested cross validation resolves the issue by selecting best performing algorithm overall.

python-mixtape

Python code for Causal inference mix tape by Prof. Scott Cunningham. https://mixtape.scunning.com/potential-outcomes.html

soybean-price-prediction-winning-solution-minneanalytics-data-science-challenge

This project includes following repositories Presentation Machine Learning algorithms like Prophet, ARIMA, XGBoost, LSTM and Seq2Seq

synthetic-tabular-data-generation

Generating Tabular Synthetic Data using State of the Art GAN architecture

tgan

Generative adversarial training for synthesizing tabular data

timeseries_seq2seq

This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.

website

https://guptapiyush340.github.io/website/